Division by 0 in `FractionalAvgPool`

### Impact An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.FractionalAvgPool`: ```python import tensorflow as tf value = tf.constant([60], shape=[1, 1, 1, 1], dtype=tf.int32) pooling_ratio = [1.0, 1.0000014345305555, 1.0, 1.0] pseudo_random = False overlapping = False deterministic = False seed = 0 seed2 = 0 tf.raw_ops.FractionalAvgPool( value=value, pooling_ratio=pooling_ratio, pseudo_random=pseudo_random, overlapping=overlapping, deterministic=deterministic, seed=seed, seed2=seed2) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L85-L89) computes a divisor quantity by dividing two user controlled values: ```cc for (int i = 0; i < tensor_in_and_out_dims; ++i) { output_size[i] = static_cast<int>(std::floor(input_size[i] / pooling_ratio_[i])); DCHECK_GT(output_size[i], 0); } ``` The user controls the values of `input_size[i]` and `pooling_ratio_[i]` (via the `value.shape()` and `pooling_ratio` arguments). If the value in `input_size[i]` is smaller than the `pooling_ratio_[i]`, then the floor operation results in `output_size[i]` being 0. The `DCHECK_GT` line is a no-op outside of debug mode, so in released versions of TF this does not trigger. Later, these computed values [are used as arguments](https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) to [`GeneratePoolingSequence`](https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). There, the first computation is a division in a modulo operation: ```cc std::vector<int64> GeneratePoolingSequence(int input_length, int output_length, GuardedPhiloxRandom* generator, bool pseudo_random) { ... if (input_length % output_length == 0) { diff = std::vector<int64>(output_length, input_length / output_length); } ... } ``` Since `output_length` can be 0, this results in runtime crashing. ### Patches We have patched the issue in GitHub commit [548b5eaf23685d86f722233d8fbc21d0a4aecb96](https://github.com/tensorflow/tensorflow/commit/548b5eaf23685d86f722233d8fbc21d0a4aecb96). The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.

CVSS 3.1

CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

Severity from

GitHub (reviewed advisory)

Weakness

CWE-369

Also known as

BIT-tensorflow-2021-29550, CVE-2021-29550, PYSEC-2021-187, PYSEC-2021-478, PYSEC-2021-676

• github.com/tensorflow/tensorflow/security/advisories/GHSA-f78g-q7r4-9wcv
• nvd.nist.gov/vuln/detail/CVE-2021-29550
• github.com/tensorflow/tensorflow/commit/548b5eaf23685d86f722233d8fbc21d0a4aecb96
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-478.yaml
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-676.yaml
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-187.yaml
• github.com/tensorflow/tensorflow